Paper 2026/2499
GhostFaceMPC: Privacy-Preserving Face Recognition with Multi-Party Computation
, University of Catania, University of Catania, University of CataniaAbstract
Facial recognition has become central to modern authentication systems, security frameworks, and personalized services. However, its widespread adoption is hindered by severe privacy risks, particularly surrounding unauthorized access to sensitive biometric data. To address these challenges, this paper presents GhostFaceMPC, the first end-to-end, privacy-preserving face recognition system to evaluate a deep convolutional neural network extractor and matching under secure multi-party computation. GhostFaceMPC enables secure processing of facial data across all stages of a face recognition process, from feature extraction and storage to matching, without exposing raw images or biometric embeddings at any stage. We introduce a multi-party computation-friendly quantization of the GhostFaceNet model and circuit optimizations that make face recognition under multi-party computation practical. The code is available at https://github.com/crypto-unict/GhostFaceMPC.
Metadata
- Available format(s)
-
PDF
- Category
- Applications
- Publication info
- Preprint.
- Keywords
- face recognitionbiometricssecure multi-party computation
- Contact author(s)
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davide carnemolla @ phd unict it
dario catalano @ unict it
mario diraimondo @ unict it - History
- 2026-10-15: approved
- 2026-10-15: received
- See all versions
- Short URL
- https://ia.cr/2026/2499
- License
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CC BY-NC-ND
BibTeX
@misc{cryptoeprint:2026/2499,
author = {Davide Carnemolla and Dario Catalano and Mario Di Raimondo},
title = {{GhostFaceMPC}: Privacy-Preserving Face Recognition with Multi-Party Computation},
howpublished = {Cryptology {ePrint} Archive, Paper 2026/2499},
year = {2026},
url = {https://eprint.iacr.org/2026/2499}
}
